import os import re import copy import inspect import diffusers from modules import shared, errors from modules.sd_samplers_common import SamplerData, flow_models debug = os.environ.get('SD_SAMPLER_DEBUG', None) is not None debug_log = shared.log.trace if debug else lambda *args, **kwargs: None # Diffusers schedulers try: from diffusers import ( CMStochasticIterativeScheduler, CosineDPMSolverMultistepScheduler, DDIMScheduler, DDPMScheduler, DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSDEScheduler, DPMSolverSinglestepScheduler, EDMDPMSolverMultistepScheduler, EDMEulerScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, FlowMatchEulerDiscreteScheduler, FlowMatchHeunDiscreteScheduler, FlowMatchLCMScheduler, HeunDiscreteScheduler, IPNDMScheduler, KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler, LCMScheduler, LMSDiscreteScheduler, PNDMScheduler, SASolverScheduler, UniPCMultistepScheduler, CogVideoXDDIMScheduler, DDIMParallelScheduler, DDPMParallelScheduler, TCDScheduler, ) except Exception as e: shared.log.error(f'Sampler import: version={diffusers.__version__} error: {e}') if os.environ.get('SD_SAMPLER_DEBUG', None) is not None: errors.display(e, 'Samplers') # SD.Next Schedulers try: # from modules.schedulers.scheduler_tcd import TCDScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_tdd import TDDScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_dc import DCSolverMultistepScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_vdm import VDMScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_dpm_flowmatch import FlowMatchDPMSolverMultistepScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_bdia import BDIA_DDIMScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_ufogen import UFOGenScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_unipc_flowmatch import FlowUniPCMultistepScheduler # pylint: disable=ungrouped-imports from modules.schedulers.scheduler_flashflow import FlashFlowMatchEulerDiscreteScheduler # pylint: disable=ungrouped-imports from modules.schedulers.perflow import PeRFlowScheduler # pylint: disable=ungrouped-imports except Exception as e: shared.log.error(f'Sampler import: version={diffusers.__version__} error: {e}') if os.environ.get('SD_SAMPLER_DEBUG', None) is not None: errors.display(e, 'Samplers') # Res4Lyf Schedulers try: from modules.res4lyf import ( ABNorsettScheduler, CommonSigmaScheduler, ETDRKScheduler, LangevinDynamicsScheduler, LawsonScheduler, PECScheduler, RESUnifiedScheduler, RESSinglestepScheduler, RESMultistepScheduler, RESSinglestepSDEScheduler, RiemannianFlowScheduler, RESDEISMultistepScheduler, LinearRKScheduler, LobattoScheduler, RadauIIAScheduler, GaussLegendreScheduler, RungeKutta44Scheduler, RungeKutta57Scheduler, RungeKutta67Scheduler, SpecializedRKScheduler, # RESMultistepSDEScheduler, # BongTangentScheduler, # SimpleExponentialScheduler, ) except Exception as e: shared.log.error(f'Sampler import: version={diffusers.__version__} error: {e}') if os.environ.get('SD_SAMPLER_DEBUG', None) is not None: errors.display(e, 'Samplers') config = { # beta_start, beta_end are typically per-scheduler, but we don't want them as they should be taken from the model itself as those are values model was trained on # prediction_type is ideally set in model as well, but it maybe needed that we do auto-detect of model type in the future 'All': { 'num_train_timesteps': 1000, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'prediction_type': 'epsilon' }, 'Res4Lyf': { 'timestep_spacing': 'linspace', "steps_offset": 0, "rescale_betas_zero_snr": False, "use_karras_sigmas": False, "use_exponential_sigmas": False, "use_beta_sigmas": False, "use_flow_sigmas": False, "shift": 1, "base_shift": 0.5, "max_shift": 1.15, "use_dynamic_shifting": False }, } config.update({ 'UniPC': { 'flow_shift': 1, 'predict_x0': True, 'sample_max_value': 1.0, 'solver_order': 2, 'solver_type': 'bh2', 'thresholding': False, 'use_beta_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_karras_sigmas': False, 'lower_order_final': True, 'timestep_spacing': 'linspace', 'final_sigmas_type': 'zero', 'rescale_betas_zero_snr': False }, 'DDIM': { 'clip_sample': False, 'set_alpha_to_one': True, 'steps_offset': 0, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False, 'thresholding': False }, 'Euler': { 'steps_offset': 0, 'interpolation_type': "linear", 'rescale_betas_zero_snr': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'use_beta_sigmas': False, 'use_exponential_sigmas': False, 'use_karras_sigmas': False }, 'Euler a': { 'steps_offset': 0, 'rescale_betas_zero_snr': False, 'timestep_spacing': 'linspace' }, 'Euler SGM': { 'steps_offset': 0, 'interpolation_type': "linear", 'rescale_betas_zero_snr': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'trailing', 'use_beta_sigmas': False, 'use_exponential_sigmas': False, 'use_karras_sigmas': False, 'prediction_type': "sample" }, 'Euler EDM': { 'sigma_schedule': "karras" }, 'Euler FlowMatch': { 'timestep_spacing': "linspace", 'shift': 1, 'use_dynamic_shifting': False, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM++': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 1 }, 'DPM++ 2M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 }, 'DPM++ 3M': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 3 }, 'DPM++ 1S': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'final_sigmas_type': 'sigma_min' }, 'DPM++ SDE': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "sde-dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 1 }, 'DPM++ 2M SDE': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "sde-dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 }, 'DPM++ 2M EDM': { 'solver_order': 2, 'solver_type': 'midpoint', 'final_sigmas_type': 'zero', 'algorithm_type': 'dpmsolver++' }, 'DPM++ Cosine': { 'solver_order': 2, 'sigma_schedule': "exponential", 'prediction_type': "v-prediction" }, 'DPM SDE': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'noise_sampler_seed': None, 'timestep_spacing': 'linspace', 'steps_offset': 0, }, 'DPM++ Inverse': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 1 }, 'DPM++ 2M Inverse': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 2 }, 'DPM++ 3M Inverse': { 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "dpmsolver++", 'solver_type': "midpoint", 'lower_order_final': True, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'final_sigmas_type': 'zero', 'timestep_spacing': 'linspace', 'solver_order': 3 }, 'UniPC FlowMatch': { 'predict_x0': True, 'sample_max_value': 1.0, 'solver_order': 2, 'solver_type': 'bh2', 'thresholding': False, 'use_beta_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_karras_sigmas': False, 'lower_order_final': True, 'timestep_spacing': 'linspace', 'final_sigmas_type': 'zero', 'rescale_betas_zero_snr': False }, 'DPM2 FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver2', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2a FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver2A', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2++ 2M FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver++2M', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2++ 2S FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver++2S', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2++ SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver++sde', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2++ 2M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 2, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver++2Msde', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'DPM2++ 3M SDE FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'solver_order': 3, 'sigma_schedule': None, 'use_beta_sigmas': False, 'algorithm_type': 'dpmsolver++3Msde', 'use_noise_sampler': True, 'beta_start': 0.00085, 'beta_end': 0.012, 'base_shift': 0.5, 'max_shift': 1.15 }, 'Heun': { 'use_beta_sigmas': False, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'timestep_spacing': 'linspace' }, 'Heun FlowMatch': { 'timestep_spacing': "linspace", 'shift': 1 }, 'LCM FlowMatch': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'thresholding': False, 'timestep_spacing': 'linspace', 'base_shift': 0.5, 'max_shift': 1.15 }, 'DEIS': { 'solver_order': 2, 'thresholding': False, 'sample_max_value': 1.0, 'algorithm_type': "deis", 'solver_type': "logrho", 'lower_order_final': True, 'timestep_spacing': 'linspace', 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_flow_sigmas': False, 'use_beta_sigmas': False }, 'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'use_flow_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace'}, 'DC Solver': { 'beta_start': 0.0001, 'beta_end': 0.02, 'solver_order': 2, 'prediction_type': "epsilon", 'thresholding': False, 'solver_type': 'bh2', 'lower_order_final': True, 'dc_order': 2, 'disable_corrector': [0] }, 'VDM Solver': { 'clip_sample_range': 2.0, }, 'TCD': { 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'beta_schedule': 'scaled_linear' }, 'TDD': { }, 'Flash FlowMatch': { 'shift': 1, 'use_dynamic_shifting': False, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'base_shift': 0.5, 'max_shift': 1.15 }, 'PeRFlow': { 'prediction_type': 'ddim_eps' }, 'UFOGen': { }, 'BDIA DDIM': { 'clip_sample': False, 'set_alpha_to_one': True, 'steps_offset': 0, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'leading', 'rescale_betas_zero_snr': False, 'thresholding': False, 'gamma': 1.0 }, 'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' }, 'IPNDM': { }, 'DDPM': { 'variance_type': "fixed_small", 'clip_sample': False, 'thresholding': False, 'clip_sample_range': 1.0, 'sample_max_value': 1.0, 'timestep_spacing': 'linspace', 'rescale_betas_zero_snr': False }, 'LMSD': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 }, 'KDPM2': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' }, 'KDPM2 a': { 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'steps_offset': 0, 'timestep_spacing': 'linspace' }, 'CMSI': { }, 'CogX DDIM': { 'beta_schedule': "scaled_linear", 'beta_start': 0.00085, 'beta_end': 0.012, 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False }, 'DDIM Parallel': {}, 'DDPM Parallel': {}, # res4lyf 'ABNorsett 2M': { 'variant': 'abnorsett_2m', **config['Res4Lyf'] }, 'ABNorsett 3M': { 'variant': 'abnorsett_3m', **config['Res4Lyf'] }, 'ABNorsett 4M': { 'variant': 'abnorsett_4m', **config['Res4Lyf'] }, 'Lawson 2S A': { 'variant': 'lawson2a_2s', **config['Res4Lyf'] }, 'Lawson 2S B': { 'variant': 'lawson2b_2s', **config['Res4Lyf'] }, 'Lawson 4S': { 'variant': 'lawson4_4s', **config['Res4Lyf'] }, 'ETD-RK 2S': { 'variant': 'etdrk2_2s', **config['Res4Lyf'] }, 'ETD-RK 3S A': { 'variant': 'etdrk3_a_3s', **config['Res4Lyf'] }, 'ETD-RK 3S B': { 'variant': 'etdrk3_b_3s', **config['Res4Lyf'] }, 'ETD-RK 4S A': { 'variant': 'etdrk4_4s', **config['Res4Lyf'] }, 'ETD-RK 4S B': { 'variant': 'etdrk4_4s_alt', **config['Res4Lyf'] }, 'RES-Unified 2M': { 'rk_type': 'res_2m', **config['Res4Lyf'] }, 'RES-Unified 3M': { 'rk_type': 'res_3m', **config['Res4Lyf'] }, 'RES-Unified 2S': { 'rk_type': 'res_2s', **config['Res4Lyf'] }, 'RES-Unified 3S': { 'rk_type': 'res_3s', **config['Res4Lyf'] }, 'RES-Singlestep 2S': { 'variant': 'res_2s', **config['Res4Lyf'] }, 'RES-Singlestep 3S': { 'variant': 'res_3s', **config['Res4Lyf'] }, 'RES-Multistep 2M': { 'variant': 'res_2m', **config['Res4Lyf'] }, 'RES-Multistep 3M': { 'variant': 'res_3m', **config['Res4Lyf'] }, 'RES-SDE 2S': { 'variant': 'res_2s', **config['Res4Lyf'] }, 'RES-SDE 3S': { 'variant': 'res_3s', **config['Res4Lyf'] }, 'DEIS-Multistep': { 'order': 2, **config['Res4Lyf'] }, 'DEIS-Unified 1S': { 'rk_type': 'deis_1s', **config['Res4Lyf'] }, 'DEIS-Unified 2M': { 'rk_type': 'deis_2m', **config['Res4Lyf'] }, 'PEC 423': { 'variant': 'pec423_2h2s', **config['Res4Lyf'] }, 'PEC 433': { 'variant': 'pec433_2h3s', **config['Res4Lyf'] }, 'Sigmoid Sigma': { 'profile': 'sigmoid', **config['Res4Lyf'] }, 'Sine Sigma': { 'profile': 'sine', **config['Res4Lyf'] }, 'Easing Sigma': { 'profile': 'easing', **config['Res4Lyf'] }, 'Arcsine Sigma': { 'profile': 'arcsine', **config['Res4Lyf'] }, 'Smoothstep Sigma': { 'profile': 'smoothstep', **config['Res4Lyf'] }, 'Langevin Dynamics': { **config['Res4Lyf'] }, 'Euclidean Flow': { 'metric_type': 'euclidean', **config['Res4Lyf'] }, 'Hyperbolic Flow': { 'metric_type': 'hyperbolic', **config['Res4Lyf'] }, 'Spherical Flow': { 'metric_type': 'spherical', **config['Res4Lyf'] }, 'Lorentzian Flow': { 'metric_type': 'lorentzian', **config['Res4Lyf'] }, 'Linear-RK 2': { 'variant': 'rk2', **config['Res4Lyf'] }, 'Linear-RK 3': { 'variant': 'rk3', **config['Res4Lyf'] }, 'Linear-RK 4': { 'variant': 'rk4', **config['Res4Lyf'] }, 'Linear-RK Euler': { 'variant': 'euler', **config['Res4Lyf'] }, 'Linear-RK Heun': { 'variant': 'heun', **config['Res4Lyf'] }, 'Linear-RK Ralston': { 'variant': 'ralston', **config['Res4Lyf'] }, 'Lobatto 2': { 'variant': 'lobatto_iiia_2s', **config['Res4Lyf'] }, 'Lobatto 3': { 'variant': 'lobatto_iiia_3s', **config['Res4Lyf'] }, 'Lobatto 4': { 'variant': 'lobatto_iiia_4s', **config['Res4Lyf'] }, 'Radau IIA 2': { 'variant': 'radau_iia_2s', **config['Res4Lyf'] }, 'Radau IIA 3': { 'variant': 'radau_iia_3s', **config['Res4Lyf'] }, 'Gauss-Legendre 2S': { 'variant': 'gauss-legendre_2s', **config['Res4Lyf'] }, 'Gauss-Legendre 3S': { 'variant': 'gauss-legendre_3s', **config['Res4Lyf'] }, 'Gauss-Legendre 4S': { 'variant': 'gauss-legendre_4s', **config['Res4Lyf'] }, 'Runge-Kutta 4/4': { **config['Res4Lyf'] }, 'Runge-Kutta 5/7': { **config['Res4Lyf'] }, 'Runge-Kutta 6/7': { **config['Res4Lyf'] }, 'Specialized-RK 3S': { 'variant': 'ssprk3_3s', **config['Res4Lyf'] }, 'Specialized-RK 4S': { 'variant': 'ssprk4_4s', **config['Res4Lyf'] }, }) samplers_data_diffusers = [ SamplerData('Default', None, [], {}), SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}), SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}), SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}), SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}), SamplerData('Euler SGM', lambda model: DiffusionSampler('Euler SGM', EulerDiscreteScheduler, model), [], {}), SamplerData('Euler EDM', lambda model: DiffusionSampler('Euler EDM', EDMEulerScheduler, model), [], {}), SamplerData('Euler FlowMatch', lambda model: DiffusionSampler('Euler FlowMatch', FlowMatchEulerDiscreteScheduler, model), [], {}), SamplerData('DPM++', lambda model: DiffusionSampler('DPM++', DPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ 3M', lambda model: DiffusionSampler('DPM++ 3M', DPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ 1S', lambda model: DiffusionSampler('DPM++ 1S', DPMSolverSinglestepScheduler, model), [], {}), SamplerData('DPM++ SDE', lambda model: DiffusionSampler('DPM++ SDE', DPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ 2M SDE', lambda model: DiffusionSampler('DPM++ 2M SDE', DPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ 2M EDM', lambda model: DiffusionSampler('DPM++ 2M EDM', EDMDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM++ Cosine', lambda model: DiffusionSampler('DPM++ 2M EDM', CosineDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM SDE', lambda model: DiffusionSampler('DPM SDE', DPMSolverSDEScheduler, model), [], {}), SamplerData('DPM++ Inverse', lambda model: DiffusionSampler('DPM++ Inverse', DPMSolverMultistepInverseScheduler, model), [], {}), SamplerData('DPM++ 2M Inverse', lambda model: DiffusionSampler('DPM++ 2M Inverse', DPMSolverMultistepInverseScheduler, model), [], {}), SamplerData('DPM++ 3M Inverse', lambda model: DiffusionSampler('DPM++ 3M Inverse', DPMSolverMultistepInverseScheduler, model), [], {}), SamplerData('UniPC FlowMatch', lambda model: DiffusionSampler('UniPC FlowMatch', FlowUniPCMultistepScheduler, model), [], {}), SamplerData('DPM2 FlowMatch', lambda model: DiffusionSampler('DPM2 FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2a FlowMatch', lambda model: DiffusionSampler('DPM2a FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2++ 2M FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2++ 2S FlowMatch', lambda model: DiffusionSampler('DPM2++ 2S FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2++ SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2++ 2M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('DPM2++ 3M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 3M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}), SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}), SamplerData('Heun FlowMatch', lambda model: DiffusionSampler('Heun FlowMatch', FlowMatchHeunDiscreteScheduler, model), [], {}), SamplerData('Flash FlowMatch', lambda model: DiffusionSampler('Flash FlowMatch', FlashFlowMatchEulerDiscreteScheduler, model), [], {}), SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}), SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, model), [], {}), SamplerData('DC Solver', lambda model: DiffusionSampler('DC Solver', DCSolverMultistepScheduler, model), [], {}), SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}), SamplerData('DDPM Parallel', lambda model: DiffusionSampler('DDPM Parallel', DDPMParallelScheduler, model), [], {}), SamplerData('DDIM Parallel', lambda model: DiffusionSampler('DDIM Parallel', DDIMParallelScheduler, model), [], {}), SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}), SamplerData('IPNDM', lambda model: DiffusionSampler('IPNDM', IPNDMScheduler, model), [], {}), SamplerData('LMSD', lambda model: DiffusionSampler('LMSD', LMSDiscreteScheduler, model), [], {}), SamplerData('KDPM2', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}), SamplerData('KDPM2 a', lambda model: DiffusionSampler('KDPM2 a', KDPM2AncestralDiscreteScheduler, model), [], {}), SamplerData('CMSI', lambda model: DiffusionSampler('CMSI', CMStochasticIterativeScheduler, model), [], {}), SamplerData('VDM Solver', lambda model: DiffusionSampler('VDM Solver', VDMScheduler, model), [], {}), SamplerData('BDIA DDIM', lambda model: DiffusionSampler('BDIA DDIM g=0', BDIA_DDIMScheduler, model), [], {}), SamplerData('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}), SamplerData('LCM FlowMatch', lambda model: DiffusionSampler('LCM FlowMatch', FlowMatchLCMScheduler, model), [], {}), SamplerData('TCD', lambda model: DiffusionSampler('TCD', TCDScheduler, model), [], {}), SamplerData('TDD', lambda model: DiffusionSampler('TDD', TDDScheduler, model), [], {}), SamplerData('PeRFlow', lambda model: DiffusionSampler('PeRFlow', PeRFlowScheduler, model), [], {}), SamplerData('UFOGen', lambda model: DiffusionSampler('UFOGen', UFOGenScheduler, model), [], {}), SamplerData('CogX DDIM', lambda model: DiffusionSampler('CogX DDIM', CogVideoXDDIMScheduler, model), [], {}), SamplerData('ABNorsett 2M', lambda model: DiffusionSampler('ABNorsett 2M', ABNorsettScheduler, model), [], {}), SamplerData('ABNorsett 3M', lambda model: DiffusionSampler('ABNorsett 3M', ABNorsettScheduler, model), [], {}), SamplerData('ABNorsett 4M', lambda model: DiffusionSampler('ABNorsett 4M', ABNorsettScheduler, model), [], {}), SamplerData('Lawson 2S A', lambda model: DiffusionSampler('Lawson 2S A', LawsonScheduler, model), [], {}), SamplerData('Lawson 2S B', lambda model: DiffusionSampler('Lawson 2S B', LawsonScheduler, model), [], {}), SamplerData('Lawson 4S', lambda model: DiffusionSampler('Lawson 4S', LawsonScheduler, model), [], {}), SamplerData('ETD-RK 2S', lambda model: DiffusionSampler('ETD-RK 2S', ETDRKScheduler, model), [], {}), SamplerData('ETD-RK 3S A', lambda model: DiffusionSampler('ETD-RK 3S A', ETDRKScheduler, model), [], {}), SamplerData('ETD-RK 3S B', lambda model: DiffusionSampler('ETD-RK 3S B', ETDRKScheduler, model), [], {}), SamplerData('ETD-RK 4S A', lambda model: DiffusionSampler('ETD-RK 4S A', ETDRKScheduler, model), [], {}), SamplerData('ETD-RK 4S B', lambda model: DiffusionSampler('ETD-RK 4S B', ETDRKScheduler, model), [], {}), SamplerData('PEC 423', lambda model: DiffusionSampler('PEC 423', PECScheduler, model), [], {}), SamplerData('PEC 433', lambda model: DiffusionSampler('PEC 433', PECScheduler, model), [], {}), SamplerData('RES-Unified 2S', lambda model: DiffusionSampler('RES-Unified 2S', RESUnifiedScheduler, model), [], {}), SamplerData('RES-Unified 3S', lambda model: DiffusionSampler('RES-Unified 3S', RESUnifiedScheduler, model), [], {}), SamplerData('RES-Unified 2M', lambda model: DiffusionSampler('RES-Unified 2M', RESUnifiedScheduler, model), [], {}), SamplerData('RES-Unified 3M', lambda model: DiffusionSampler('RES-Unified 3M', RESUnifiedScheduler, model), [], {}), SamplerData('RES-Singlestep 2S', lambda model: DiffusionSampler('RES-Singlestep 2S', RESSinglestepScheduler, model), [], {}), SamplerData('RES-Singlestep 3S', lambda model: DiffusionSampler('RES-Singlestep 3S', RESSinglestepScheduler, model), [], {}), SamplerData('RES-Multistep 2M', lambda model: DiffusionSampler('RES-Multistep 2M', RESMultistepScheduler, model), [], {}), SamplerData('RES-Multistep 3M', lambda model: DiffusionSampler('RES-Multistep 3M', RESMultistepScheduler, model), [], {}), SamplerData('RES-SDE 2S', lambda model: DiffusionSampler('RES-SDE 2S', RESSinglestepSDEScheduler, model), [], {}), SamplerData('RES-SDE 3S', lambda model: DiffusionSampler('RES-SDE 3S', RESSinglestepSDEScheduler, model), [], {}), SamplerData('DEIS-Multistep', lambda model: DiffusionSampler('DEIS Multistep', RESDEISMultistepScheduler, model), [], {}), SamplerData('DEIS-Unified 1S', lambda model: DiffusionSampler('DEIS-Unified 1S', RESUnifiedScheduler, model), [], {}), SamplerData('DEIS-Unified 2M', lambda model: DiffusionSampler('DEIS-Unified 2M', RESUnifiedScheduler, model), [], {}), SamplerData('Sigmoid Sigma', lambda model: DiffusionSampler('Sigmoid Sigma', CommonSigmaScheduler, model), [], {}), SamplerData('Sine Sigma', lambda model: DiffusionSampler('Sine Sigma', CommonSigmaScheduler, model), [], {}), SamplerData('Easing Sigma', lambda model: DiffusionSampler('Easing Sigma', CommonSigmaScheduler, model), [], {}), SamplerData('Arcsine Sigma', lambda model: DiffusionSampler('Arcsine Sigma', CommonSigmaScheduler, model), [], {}), SamplerData('Smoothstep Sigma', lambda model: DiffusionSampler('Smoothstep Sigma', CommonSigmaScheduler, model), [], {}), SamplerData('Langevin Dynamics', lambda model: DiffusionSampler('Langevin Dynamics', LangevinDynamicsScheduler, model), [], {}), SamplerData('Euclidean Flow', lambda model: DiffusionSampler('Euclidean Flow', RiemannianFlowScheduler, model), [], {}), SamplerData('Hyperbolic Flow', lambda model: DiffusionSampler('Hyperbolic Flow', RiemannianFlowScheduler, model), [], {}), SamplerData('Spherical Flow', lambda model: DiffusionSampler('Spherical Flow', RiemannianFlowScheduler, model), [], {}), SamplerData('Lorentzian Flow', lambda model: DiffusionSampler('Lorentzian Flow', RiemannianFlowScheduler, model), [], {}), SamplerData('Linear-RK 2', lambda model: DiffusionSampler('Linear-RK 2', LinearRKScheduler, model), [], {}), SamplerData('Linear-RK 3', lambda model: DiffusionSampler('Linear-RK 3', LinearRKScheduler, model), [], {}), SamplerData('Linear-RK 4', lambda model: DiffusionSampler('Linear-RK 4', LinearRKScheduler, model), [], {}), SamplerData('Linear-RK Euler', lambda model: DiffusionSampler('Linear-RK Euler', LinearRKScheduler, model), [], {}), SamplerData('Linear-RK Heun', lambda model: DiffusionSampler('Linear-RK Heun', LinearRKScheduler, model), [], {}), SamplerData('Linear-RK Ralston', lambda model: DiffusionSampler('Linear-RK Ralston', LinearRKScheduler, model), [], {}), SamplerData('Lobatto 2', lambda model: DiffusionSampler('Lobatto 2', LobattoScheduler, model), [], {}), SamplerData('Lobatto 3', lambda model: DiffusionSampler('Lobatto 3', LobattoScheduler, model), [], {}), SamplerData('Lobatto 4', lambda model: DiffusionSampler('Lobatto 4', LobattoScheduler, model), [], {}), SamplerData('Radau IIA 2', lambda model: DiffusionSampler('Radau IIA 2', RadauIIAScheduler, model), [], {}), SamplerData('Radau IIA 3', lambda model: DiffusionSampler('Radau IIA 2', RadauIIAScheduler, model), [], {}), SamplerData('Radau IIA 4', lambda model: DiffusionSampler('Radau IIA 2', RadauIIAScheduler, model), [], {}), SamplerData('Gauss-Legendre 2S', lambda model: DiffusionSampler('Gauss-Legendre 2S', GaussLegendreScheduler, model), [], {}), SamplerData('Gauss-Legendre 3S', lambda model: DiffusionSampler('Gauss-Legendre 3S', GaussLegendreScheduler, model), [], {}), SamplerData('Gauss-Legendre 4S', lambda model: DiffusionSampler('Gauss-Legendre 4S', GaussLegendreScheduler, model), [], {}), SamplerData('Specialized-RK 3S', lambda model: DiffusionSampler('Specialized-RK 3S', SpecializedRKScheduler, model), [], {}), SamplerData('Specialized-RK 4S', lambda model: DiffusionSampler('Specialized-RK 4S', SpecializedRKScheduler, model), [], {}), SamplerData('Runge-Kutta 4/4', lambda model: DiffusionSampler('Runge-Kutta 4/4', RungeKutta44Scheduler, model), [], {}), SamplerData('Runge-Kutta 5/7', lambda model: DiffusionSampler('Runge-Kutta 5/7', RungeKutta57Scheduler, model), [], {}), SamplerData('Runge-Kutta 6/7', lambda model: DiffusionSampler('Runge-Kutta 6/7', RungeKutta67Scheduler, model), [], {}), SamplerData('Same as primary', None, [], {}), ] class DiffusionSampler: def __init__(self, name, constructor, model, **kwargs): if name == 'Default': return self.name = name self.config = {} self.sampler = None if getattr(model, "default_scheduler", None) is None and (model is not None): # sanity check model.default_scheduler = copy.deepcopy(model.scheduler) for key, value in config.get('All', {}).items(): # apply global defaults self.config[key] = value debug_log(f'Sampler: all="{self.config}"') if model is None: orig_config = {} elif hasattr(model.default_scheduler, 'scheduler_config'): # find model defaults orig_config = model.default_scheduler.scheduler_config else: orig_config = model.default_scheduler.config debug_log(f'Sampler: diffusers="{self.config}"') debug_log(f'Sampler: original="{orig_config}"') for key, value in orig_config.items(): # apply model defaults if key in self.config: self.config[key] = value debug_log(f'Sampler: default="{self.config}"') for key, value in config.get(name, {}).items(): # apply diffusers per-scheduler defaults self.config[key] = value for key, value in kwargs.items(): # apply user args, if any if key in self.config: self.config[key] = value # finally apply user preferences if shared.opts.schedulers_prediction_type != 'default': self.config['prediction_type'] = shared.opts.schedulers_prediction_type if shared.opts.schedulers_beta_schedule != 'default': if shared.opts.schedulers_beta_schedule == 'linear': self.config['beta_schedule'] = 'linear' elif shared.opts.schedulers_beta_schedule == 'scaled': self.config['beta_schedule'] = 'scaled_linear' elif shared.opts.schedulers_beta_schedule == 'cosine': self.config['beta_schedule'] = 'squaredcos_cap_v2' elif shared.opts.schedulers_beta_schedule == 'sigmoid': self.config['beta_schedule'] = 'sigmoid' timesteps = re.split(',| ', shared.opts.schedulers_timesteps) timesteps = [int(x) for x in timesteps if x.isdigit()] if len(timesteps) == 0: if 'sigma_schedule' in self.config: self.config['sigma_schedule'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != 'default' else None if shared.opts.schedulers_sigma == 'default' and shared.sd_model_type in flow_models and 'use_flow_sigmas' in self.config: self.config['use_flow_sigmas'] = True elif shared.opts.schedulers_sigma == 'betas' and 'use_beta_sigmas' in self.config: self.config['use_beta_sigmas'] = True elif shared.opts.schedulers_sigma == 'karras' and 'use_karras_sigmas' in self.config: self.config['use_karras_sigmas'] = True elif shared.opts.schedulers_sigma == 'flowmatch' and 'use_flow_sigmas' in self.config: self.config['use_flow_sigmas'] = True elif shared.opts.schedulers_sigma == 'exponential' and 'use_exponential_sigmas' in self.config: self.config['use_exponential_sigmas'] = True elif shared.opts.schedulers_sigma == 'lambdas' and 'use_lu_lambdas' in self.config: self.config['use_lu_lambdas'] = True else: pass # timesteps are set using set_timesteps in set_pipeline_args if 'thresholding' in self.config: self.config['thresholding'] = shared.opts.schedulers_use_thresholding if 'lower_order_final' in self.config: self.config['lower_order_final'] = shared.opts.schedulers_use_loworder if 'solver_order' in self.config and int(shared.opts.schedulers_solver_order) > 0: self.config['solver_order'] = int(shared.opts.schedulers_solver_order) if 'predict_x0' in self.config: self.config['solver_type'] = shared.opts.uni_pc_variant if 'beta_start' in self.config and shared.opts.schedulers_beta_start > 0: self.config['beta_start'] = shared.opts.schedulers_beta_start if 'beta_end' in self.config and shared.opts.schedulers_beta_end > 0: self.config['beta_end'] = shared.opts.schedulers_beta_end if 'shift' in self.config: self.config['shift'] = shared.opts.schedulers_shift if shared.opts.schedulers_shift > 0 else 3 if 'flow_shift' in self.config: self.config['flow_shift'] = shared.opts.schedulers_shift if shared.opts.schedulers_shift > 0 else 3 if 'use_dynamic_shifting' in self.config: self.config['use_dynamic_shifting'] = True if shared.opts.schedulers_shift == 0 else shared.opts.schedulers_dynamic_shift if 'base_shift' in self.config: self.config['base_shift'] = shared.opts.schedulers_base_shift if 'max_shift' in self.config: self.config['max_shift'] = shared.opts.schedulers_max_shift if 'use_beta_sigmas' in self.config and 'sigma_schedule' in self.config: self.config['use_beta_sigmas'] = 'StableDiffusion3' in model.__class__.__name__ if 'rescale_betas_zero_snr' in self.config: self.config['rescale_betas_zero_snr'] = shared.opts.schedulers_rescale_betas if 'timestep_spacing' in self.config and shared.opts.schedulers_timestep_spacing != 'default' and shared.opts.schedulers_timestep_spacing is not None: self.config['timestep_spacing'] = shared.opts.schedulers_timestep_spacing if 'num_train_timesteps' in self.config: self.config['num_train_timesteps'] = shared.opts.schedulers_timesteps_range if 'EDM' in name: del self.config['beta_start'] del self.config['beta_end'] del self.config['beta_schedule'] if name in {'IPNDM', 'CMSI', 'VDM Solver'}: del self.config['beta_start'] del self.config['beta_end'] del self.config['beta_schedule'] del self.config['prediction_type'] if 'prediction_type' in self.config and 'Flow' in name: self.config['prediction_type'] = 'flow_prediction' if 'SGM' in name: self.config['timestep_spacing'] = 'trailing' # validate all config params signature = inspect.signature(constructor, follow_wrapped=True) possible = signature.parameters.keys() for key in self.config.copy().keys(): if key not in possible: del self.config[key] debug_log(f'Sampler: name="{name}"') debug_log(f'Sampler: config={self.config}') debug_log(f'Sampler: signature={possible}') # finally create the new sampler try: sampler = constructor(**self.config) except Exception as e: shared.log.error(f'Sampler: "{name}" {e}') if debug: errors.display(e, 'Samplers') self.sampler = None return if hasattr(sampler, 'set_timesteps'): accept_sigmas = "sigmas" in set(inspect.signature(sampler.set_timesteps).parameters.keys()) accepts_timesteps = "timesteps" in set(inspect.signature(sampler.set_timesteps).parameters.keys()) accept_scale_noise = hasattr(sampler, "scale_noise") debug_log(f'Sampler: "{name}" sigmas={accept_sigmas} timesteps={accepts_timesteps}') if ('Flux' in model.__class__.__name__) and (not accept_sigmas): shared.log.warning(f'Sampler: "{name}" does not accept sigmas') self.sampler = None return if ('StableDiffusion3' in model.__class__.__name__) and (not accept_scale_noise): shared.log.warning(f'Sampler: "{name}" does not implement scale noise') self.sampler = None return # monkey-patch to allow sdxl pipeline to execute flowmatch samplers if not hasattr(sampler, 'scale_model_input'): sampler.scale_model_input = lambda x, _y: x if not hasattr(sampler, 'init_noise_sigma'): sampler.init_noise_sigma = 1.0 self.sampler = sampler # shared.log.debug_log(f'Sampler: class="{self.sampler.__class__.__name__}" config={self.sampler.config}') self.sampler.name = name